Given the need for data initiatives to arise from and maintain alignment with the business, a dedicated hive of data scientists may not be the best approach. As Hadaway points out, "Hiring data scientists will not make you successful. The rest of the organization also needs to know how to use data."
"People have been hiring up data scientists like crazy but don’t really know where they fit in the organization, so they park them in a shared services team, or maybe as part of the marketing or finance department," Collier said. "The data scientists sit in their silo, not really interacting well with the business to figure out what's important and what problems they’re challenged with."
Enabling these interactions and generating business-ready insights requires not only an open data architecture, but also cohesive teams. To work well with data, teams need to be driven by a shared charter and defined outcomes, and include both technologists and business leaders.
"You have to be willing to break down those walls between IT and business and structure cross-functional teams that are chartered to build products," Collier said. "Then you have to put the right people on those teams, not only to do advanced machine learning, but also business experts who can say ‘Yeah, this machine learning matters, and this machine learning model actually does something useful and meaningful.'"
This process works both ways. Just as data scientists need to grasp business goals, other parts of the business need to recognize that data is a shared resource and responsibility. Basic elements of data governance, such as defining who has ultimate ownership over and access to data, are typically established and managed from the top down. But people in every business line should be examining data generated in the course of daily operations to determine the role it might play in decision-making and ensure consistent standards are maintained.
"This is what we call data democratization," Windheuser said."Everybody is able to utilize the data, but everybody is also responsible for cleaning it, for taking care of it, so the quality is there and it’s up to date. It’s like a family living in a house."
This philosophy should extend to the wrangling and organizing of data so it’s fit for business purposes. This is a decidedly unsexy task, often referred to as "munging," and typically reserved for technologists.
"Business users have to be involved in the process because they’re the ones who understand why something is dirty," Parsons said. "You also need technologists, hopefully with a long history in the organization, because particularly for data that’s been around multiple years, there are all kinds of traps. So you need someone who understands the history of the systems, but also somebody who understands what the data actually means in the business context to resolve the problems that come up." As outlined in the latest edition of ThoughtWorks Technology Radar, new data tools and innovations are just more buzzwords in isolation. What spurs progress is figuring out how to meld these data innovations with enduring engineering practices in well-defined teams.